library(MASS)     # Datasets
library(mice)     # Boys dataset
library(dplyr)    # Data manipulation
library(magrittr) # Pipes
library(ggplot2)  # Plotting suite
library(sf)       # Spatial features

• R makes it very easy to visualise data
• But fine-tuning figures to specific standards can take a lot of time

Why visualise?

• We can process a lot of information quickly with our eyes
• More intuitively accessible to laymen
• Plots give us information about
  • Distribution / shape
  • Irregularities
  • Assumptions
  • Intuitions

• A few plots in base graphics in R
• Plotting with ggplot2 graphics
• Plotting data on maps

First, recall:

• Vectors

height <- c(50.1, 53.5, 50.0, 54.5, 57.5)
weight <- c(3.65, 3.37, 3.14, 4.27, 5.03)

• Data frames

boys <- boys
head(boys)

##      age  hgt   wgt   bmi   hc  gen  phb tv   reg
## 3  0.035 50.1 3.650 14.54 33.7 <NA> <NA> NA south
## 4  0.038 53.5 3.370 11.77 35.0 <NA> <NA> NA south
## 18 0.057 50.0 3.140 12.56 35.2 <NA> <NA> NA south
## 23 0.060 54.5 4.270 14.37 36.7 <NA> <NA> NA south
## 28 0.062 57.5 5.030 15.21 37.3 <NA> <NA> NA south
## 36 0.068 55.5 4.655 15.11 37.0 <NA> <NA> NA south

To call a variable in the data frame, use the $ notation:

boys$hgt[1:10]

##  [1] 50.1 53.5 50.0 54.5 57.5 55.5 52.5 53.0 55.1 54.5

plot(x = boys$hgt, y = boys$wgt, main = "Scatter plot", 
     xlab = "Height", ylab = "Weight", bty = "L")

plot(x = 1:5, y = exp(1:5), type = "l", main = "Line chart", bty = "L")

counts <- table(boys$reg)

barplot(counts, main="Bar chart", ylab = "N")

counts <- table(boys$reg)

pie(x=counts, main="Pie chart")

hist(boys$hgt, main = "Histogram", xlab = "Height")

boxplot(boys$hgt ~ boys$reg, main = "Boxplot", 
        xlab = "Region", ylab = "Height")

Layered plotting based on the book The Grammar of Graphics by Leland Wilkinsons.

With ggplot2 you

1. provide the data
2. define how to map variables to aesthetics
3. state which geometric object to display
4. (optional) edit the overall theme of the plot

ggplot2 then takes care of the details

1: Provide the data

boys %>%
  ggplot()

2: map variable to aesthetics

boys %>%
  ggplot(aes(x = age, y = bmi))

3: state which geometric object to display

boys %>%
  ggplot(aes(x = age, y = bmi)) +
  geom_point()

Create the plot

gg <- 
  boys %>%
  ggplot(aes(x = age, y = bmi)) +
  geom_point()

Add another layer (smooth fit line)

gg <- gg + 
  geom_smooth(col = "dark blue")

Give it some labels and a nice look

gg <- gg + 
  labs(x = "Age", y = "BMI", title = "BMI trend for boys") +
  theme_minimal()

plot(gg)

• x
• y
• size
• colour
• fill
• opacity (alpha)
• linetype
• …

gg <- 
  boys %>% 
  filter(!is.na(reg)) %>% 
  
  ggplot(aes(x      = age, 
             y      = bmi, 
             size   = hc, 
             colour = reg)) +
  
  geom_point(alpha = 0.5) +
  
  labs(title  = "BMI trend for boys",
       x      = "Age", 
       y      = "BMI", 
       size   = "Head circumference",
       colour = "Region") +
  theme_minimal()

plot(gg)

• geom_point
• geom_bar
• geom_line

• geom_smooth

• geom_histogram
• geom_boxplot

• geom_density

• A formal standard (ISO 19125-1:2004) that describes how objects in the real world can be represented in computers, with emphasis on the spatial geometry of these objects.
• As implemented e.g. in ArcGIS
• Implemented for R in the sf package
• Feature geometries are stored in data.frames

We have time for a cursory introduction at most.

denmark <- st_read("DK_map.shp")

plot(st_geometry(denmark))

denmark$proportion.over.70 <- denmark$over70/denmark$population

plot(denmark["proportion.over.70"],
     main = "Proportion of population aged 70 years and above")

denmark %>% ggplot(aes(fill=proportion.over.70)) + geom_sf()